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2014


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Juggling revisited — A voxel based morphometry study with expert jugglers

Gerber, P., Schlaffke, L., Heba, S., Greenlee, M., Schultz, T., Schmidt-Wilcke, T.

NeuroImage, 95, pages: 320-325, 2014 (article)

ei

Web DOI [BibTex]

2014


Web DOI [BibTex]


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Assessing attention and cognitive function in completely locked-in state with event-related brain potentials and epidural electrocorticography

Bensch, M., Martens, S., Halder, S., Hill, J., Nijboer, F., Ramos, A., Birbaumer, N., Bodgan, M., Kotchoubey, B., Rosenstiel, W., Schölkopf, B., Gharabaghi, A.

Journal of Neural Engineering, 11(2):026006, 2014 (article)

Abstract
Objective. Patients in the completely locked-in state (CLIS), due to, for example, amyotrophic lateral sclerosis (ALS), no longer possess voluntary muscle control. Assessing attention and cognitive function in these patients during the course of the disease is a challenging but essential task for both nursing staff and physicians. Approach. An electrophysiological cognition test battery, including auditory and semantic stimuli, was applied in a late-stage ALS patient at four different time points during a six-month epidural electrocorticography (ECoG) recording period. Event-related cortical potentials (ERP), together with changes in the ECoG signal spectrum, were recorded via 128 channels that partially covered the left frontal, temporal and parietal cortex. Main results. Auditory but not semantic stimuli induced significant and reproducible ERP projecting to specific temporal and parietal cortical areas. N1/P2 responses could be detected throughout the whole study period. The highest P3 ERP was measured immediately after the patient's last communication through voluntary muscle control, which was paralleled by low theta and high gamma spectral power. Three months after the patient's last communication, i.e., in the CLIS, P3 responses could no longer be detected. At the same time, increased activity in low-frequency bands and a sharp drop of gamma spectral power were recorded. Significance. Cortical electrophysiological measures indicate at least partially intact attention and cognitive function during sparse volitional motor control for communication. Although the P3 ERP and frequency-specific changes in the ECoG spectrum may serve as indicators for CLIS, a close-meshed monitoring will be required to define the exact time point of the transition.

ei

DOI [BibTex]

DOI [BibTex]


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Identifiability of Gaussian Structural Equation Models with Equal Error Variances

Peters, J., Bühlman, P.

Biometrika, 101(1):219-228, 2014 (article)

ei

DOI [BibTex]


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Quantifying the effect of intertrial dependence on perceptual decisions

Fründ, I., Wichmann, F., Macke, J.

Journal of Vision, 14(7):1-16, 2014 (article)

ei

Web PDF link (url) DOI [BibTex]

Web PDF link (url) DOI [BibTex]


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Two numerical models designed to reproduce Saturn ring temperatures as measured by Cassini-CIRS

Altobelli, N., Lopez-Paz, D., Pilorz, S., Spilker, L., Morishima, R., Brooks, S., Leyrat, C., Deau, E., Edgington, S., Flandes, A.

Icarus, 238(0):205 - 220, 2014 (article)

ei

Web link (url) DOI [BibTex]

Web link (url) DOI [BibTex]


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CAM: Causal Additive Models, high-dimensional order search and penalized regression

Bühlmann, P., Peters, J., Ernest, J.

Annals of Statistics, 42(6):2526-2556, 2014 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Policy Evaluation with Temporal Differences: A Survey and Comparison

Dann, C., Neumann, G., Peters, J.

Journal of Machine Learning Research, 15, pages: 809-883, 2014 (article)

ei

PDF [BibTex]

PDF [BibTex]


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Uncovering the Structure and Temporal Dynamics of Information Propagation

Gomez Rodriguez, M., Leskovec, J., Balduzzi, D., Schölkopf, B.

Network Science, 2(1):26-65, 2014 (article)

Abstract
Time plays an essential role in the diffusion of information, influence, and disease over networks. In many cases we can only observe when a node is activated by a contagion—when a node learns about a piece of information, makes a decision, adopts a new behavior, or becomes infected with a disease. However, the underlying network connectivity and transmission rates between nodes are unknown. Inferring the underlying diffusion dynamics is important because it leads to new insights and enables forecasting, as well as influencing or containing information propagation. In this paper we model diffusion as a continuous temporal process occurring at different rates over a latent, unobserved network that may change over time. Given information diffusion data, we infer the edges and dynamics of the underlying network. Our model naturally imposes sparse solutions and requires no parameter tuning. We develop an efficient inference algorithm that uses stochastic convex optimization to compute online estimates of the edges and transmission rates. We evaluate our method by tracking information diffusion among 3.3 million mainstream media sites and blogs, and experiment with more than 179 million different instances of information spreading over the network in a one-year period. We apply our network inference algorithm to the top 5,000 media sites and blogs and report several interesting observations. First, information pathways for general recurrent topics are more stable across time than for on-going news events. Second, clusters of news media sites and blogs often emerge and vanish in a matter of days for on-going news events. Finally, major events, for example, large scale civil unrest as in the Libyan civil war or Syrian uprising, increase the number of information pathways among blogs, and also increase the network centrality of blogs and social media sites.

ei

DOI [BibTex]


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Causal discovery via reproducing kernel Hilbert space embeddings

Chen, Z., Zhang, K., Chan, L., Schölkopf, B.

Neural Computation, 26(7):1484-1517, 2014 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Impact of Large-Scale Climate Extremes on Biospheric Carbon Fluxes: An Intercomparison Based on MsTMIP Data

Zscheischler, J., Michalak, A., Schwalm, M., Mahecha, M., Huntzinger, D., Reichstein, M., Berthier, G., Ciais, P., Cook, R., El-Masri, B., Huang, M., Ito, A., Jain, A., King, A., Lei, H., Lu, C., Mao, J., Peng, S., Poulter, B., Ricciuto, D., Shi, X., Tao, B., Tian, H., Viovy, N., Wang, W., Wei, Y., Yang, J., Zeng, N.

Global Biogeochemical Cycles, 2014 (article)

ei

Web DOI [BibTex]

Web DOI [BibTex]


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A Brain-Computer Interface Based on Self-Regulation of Gamma-Oscillations in the Superior Parietal Cortex

Grosse-Wentrup, M., Schölkopf, B.

Journal of Neural Engineering, 11(5):056015, 2014 (article)

Abstract
Objective. Brain–computer interface (BCI) systems are often based on motor- and/or sensory processes that are known to be impaired in late stages of amyotrophic lateral sclerosis (ALS). We propose a novel BCI designed for patients in late stages of ALS that only requires high-level cognitive processes to transmit information from the user to the BCI. Approach. We trained subjects via EEG-based neurofeedback to self-regulate the amplitude of gamma-oscillations in the superior parietal cortex (SPC). We argue that parietal gamma-oscillations are likely to be associated with high-level attentional processes, thereby providing a communication channel that does not rely on the integrity of sensory- and/or motor-pathways impaired in late stages of ALS. Main results. Healthy subjects quickly learned to self-regulate gamma-power in the SPC by alternating between states of focused attention and relaxed wakefulness, resulting in an average decoding accuracy of 70.2%. One locked-in ALS patient (ALS-FRS-R score of zero) achieved an average decoding accuracy significantly above chance-level though insufficient for communication (55.8%). Significance. Self-regulation of gamma-power in the SPC is a feasible paradigm for brain–computer interfacing and may be preserved in late stages of ALS. This provides a novel approach to testing whether completely locked-in ALS patients retain the capacity for goal-directed thinking.

ei

Web DOI [BibTex]


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On power law distributions in large-scale taxonomies

Babbar, R., Metzig, C., Partalas, I., Gaussier, E., Amini, M.

SIGKDD Explorations, Special Issue on Big Data, 16(1):47-56, 2014 (article)

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[BibTex]

[BibTex]


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Predicting Motor Learning Performance from Electroencephalographic Data

Meyer, T., Peters, J., Zander, T., Schölkopf, B., Grosse-Wentrup, M.

Journal of NeuroEngineering and Rehabilitation, 11:24, 2014 (article)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Special issue on autonomous grasping and manipulation

Ben Amor, H., Saxena, A., Hudson, N., Peters, J.

Autonomous Robots, 36(1-2):1-3, 2014 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Evaluation of Positron Emission Tomographic Tracers for Imaging of Papillomavirus-Induced Tumors in Rabbits

Probst, S., Wiehr, S., Mantlik, F., Schmidt, H., Kolb, A., Münch, P., Delcuratolo, M., Stubenrauch, F., Pichler, B., Iftner, T.

Molecular Imaging, 13(1):1536-0121, 2014 (article)

ei

Web [BibTex]

Web [BibTex]


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Extreme events in gross primary production: a characterization across continents

Zscheischler, J., Reichstein, M., Harmeling, S., Rammig, A., Tomelleri, E., Mahecha, M.

Biogeosciences, 11, pages: 2909-2924, 2014 (article)

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PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Indirect Robot Model Learning for Tracking Control

Bocsi, B., Csató, L., Peters, J.

Advanced Robotics, 28(9):589-599, 2014 (article)

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PDF DOI [BibTex]


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An extended approach for spatiotemporal gapfilling: dealing with large and systematic gaps in geoscientific datasets

v Buttlar, J., Zscheischler, J., Mahecha, M.

Nonlinear Processes in Geophysics, 21(1):203-215, 2014 (article)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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On the Quantification Accuracy, Homogeneity, and Stability of Simultaneous Positron Emission Tomography/Magnetic Resonance Imaging Systems

Schmidt, H., Schwenzer, N., Bezrukov, I., Mantlik, F., Kolb, A., Kupferschläger, J., Pichler, B.

Investigative Radiology, 49(6):373-381, 2014 (article)

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Natural Evolution Strategies

Wierstra, D., Schaul, T., Glasmachers, T., Sun, Y., Peters, J., Schmidhuber, J.

Journal of Machine Learning Research, 15, pages: 949-980, 2014 (article)

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PDF [BibTex]

PDF [BibTex]


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Factors controlling decomposition rates of fine root litter in temperate forests and grasslands

Solly, E., Schöning, I., Boch, S., Kandeler, E., Marhan, S., Michalzik, B., Müller, J., Zscheischler, J., Trumbore, S., Schrumpf, M.

Plant and Soil, 2014 (article)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Causal Discovery with Continuous Additive Noise Models

Peters, J., Mooij, J., Janzing, D., Schölkopf, B.

Journal of Machine Learning Research, 15, pages: 2009-2053, 2014 (article)

ei

PDF Web [BibTex]

PDF Web [BibTex]


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A few extreme events dominate global interannual variability in gross primary production

Zscheischler, J., Mahecha, M., v Buttlar, J., Harmeling, S., Jung, M., Rammig, A., Randerson, J., Schölkopf, B., Seneviratne, S., Tomelleri, E., Zaehle, S., Reichstein, M.

Environmental Research Letters, 9(3):035001, 2014 (article)

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Kernel methods in system identification, machine learning and function estimation: A survey

Pillonetto, G., Dinuzzo, F., Chen, T., De Nicolao, G., Ljung, L.

Automatica, 50(3):657-682, 2014 (article)

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Development of a novel depth of interaction PET detector using highly multiplexed G-APD cross-strip encoding

Kolb, A., Parl, C., Mantlik, F., Liu, C., Lorenz, E., Renker, D., Pichler, B.

Medical Physics, 41(8), 2014 (article)

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Epidural electrocorticography for monitoring of arousal in locked-in state

Martens, S., Bensch, M., Halder, S., Hill, J., Nijboer, F., Ramos-Murguialday, A., Schölkopf, B., Birbaumer, N., Gharabaghi, A.

Frontiers in Human Neuroscience, 8(861), 2014 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Simultaneous Whole-Body PET/MR Imaging in Comparison to PET/CT in Pediatric Oncology: Initial Results

Schäfer, J. F., Gatidis, S., Schmidt, H., Gückel, B., Bezrukov, I., Pfannenberg, C. A., Reimold, M., M., E., Fuchs, J., Claussen, C. D., Schwenzer, N. F.

Radiology, 273(1):220-231, 2014 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Cost-Sensitive Active Learning With Lookahead: Optimizing Field Surveys for Remote Sensing Data Classification

Persello, C., Boularias, A., Dalponte, M., Gobakken, T., Naesset, E., Schölkopf, B.

IEEE Transactions on Geoscience and Remote Sensing, 10(52):6652 - 6664, 2014 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Principles of PET/MR Imaging

Disselhorst, J. A., Bezrukov, I., Kolb, A., Parl, C., Pichler, B. J.

Journal of Nuclear Medicine, 55(6, Supplement 2):2S-10S, 2014 (article)

ei

DOI [BibTex]

DOI [BibTex]


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IM3SHAPE: Maximum likelihood galaxy shear measurement code for cosmic gravitational lensing

Zuntz, J., Kacprzak, T., Voigt, L., Hirsch, M., Rowe, B., Bridle, S.

Astrophysics Source Code Library, 1, pages: 09013, 2014 (article)

ei

link (url) [BibTex]

link (url) [BibTex]


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Efficient nearest neighbors via robust sparse hashing

Cherian, A., Sra, S., Morellas, V., Papanikolopoulos, N.

IEEE Transactions on Image Processing, 23(8):3646-3655, 2014 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Sérsic galaxy models in weak lensing shape measurement: model bias, noise bias and their interaction

Kacprzak, T., Bridle, S., Rowe, B., Voigt, L., Zuntz, J., Hirsch, M., MacCrann, N.

Monthly Notices of the Royal Astronomical Society, 441(3):2528-2538, Oxford University Press, 2014 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Diminished White Matter Integrity in Patients with Systemic Lupus Erythematosus

Schmidt-Wilcke, T., Cagnoli, P., Wang, P., Schultz, T., Lotz, A., Mccune, W. J., Sundgren, P. C.

NeuroImage: Clinical, 5, pages: 291-297, 2014 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Information-Theoretic Bounded Rationality and ϵ-Optimality

Braun, DA, Ortega, PA

Entropy, 16(8):4662-4676, August 2014 (article)

Abstract
Bounded rationality concerns the study of decision makers with limited information processing resources. Previously, the free energy difference functional has been suggested to model bounded rational decision making, as it provides a natural trade-off between an energy or utility function that is to be optimized and information processing costs that are measured by entropic search costs. The main question of this article is how the information-theoretic free energy model relates to simple \(\epsilon\)-optimality models of bounded rational decision making, where the decision maker is satisfied with any action in an \(\epsilon\)-neighborhood of the optimal utility. We find that the stochastic policies that optimize the free energy trade-off comply with the notion of \(\epsilon\)-optimality. Moreover, this optimality criterion even holds when the environment is adversarial. We conclude that the study of bounded rationality based on \(\epsilon\)-optimality criteria that abstract away from the particulars of the information processing constraints is compatible with the information-theoretic free energy model of bounded rationality.

ei

DOI [BibTex]

DOI [BibTex]


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Occam’s Razor in sensorimotor learning

Genewein, T, Braun, D

Proceedings of the Royal Society of London B, 281(1783):1-7, May 2014 (article)

Abstract
A large number of recent studies suggest that the sensorimotor system uses probabilistic models to predict its environment and makes inferences about unobserved variables in line with Bayesian statistics. One of the important features of Bayesian statistics is Occam's Razor—an inbuilt preference for simpler models when comparing competing models that explain some observed data equally well. Here, we test directly for Occam's Razor in sensorimotor control. We designed a sensorimotor task in which participants had to draw lines through clouds of noisy samples of an unobserved curve generated by one of two possible probabilistic models—a simple model with a large length scale, leading to smooth curves, and a complex model with a short length scale, leading to more wiggly curves. In training trials, participants were informed about the model that generated the stimulus so that they could learn the statistics of each model. In probe trials, participants were then exposed to ambiguous stimuli. In probe trials where the ambiguous stimulus could be fitted equally well by both models, we found that participants showed a clear preference for the simpler model. Moreover, we found that participants’ choice behaviour was quantitatively consistent with Bayesian Occam's Razor. We also show that participants’ drawn trajectories were similar to samples from the Bayesian predictive distribution over trajectories and significantly different from two non-probabilistic heuristics. In two control experiments, we show that the preference of the simpler model cannot be simply explained by a difference in physical effort or by a preference for curve smoothness. Our results suggest that Occam's Razor is a general behavioural principle already present during sensorimotor processing.

ei

DOI [BibTex]

DOI [BibTex]


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Generalized Thompson sampling for sequential decision-making and causal inference

Ortega, PA, Braun, DA

Complex Adaptive Systems Modeling, 2(2):1-23, March 2014 (article)

Abstract
Purpose Sampling an action according to the probability that the action is believed to be the optimal one is sometimes called Thompson sampling. Methods Although mostly applied to bandit problems, Thompson sampling can also be used to solve sequential adaptive control problems, when the optimal policy is known for each possible environment. The predictive distribution over actions can then be constructed by a Bayesian superposition of the policies weighted by their posterior probability of being optimal. Results Here we discuss two important features of this approach. First, we show in how far such generalized Thompson sampling can be regarded as an optimal strategy under limited information processing capabilities that constrain the sampling complexity of the decision-making process. Second, we show how such Thompson sampling can be extended to solve causal inference problems when interacting with an environment in a sequential fashion. Conclusion In summary, our results suggest that Thompson sampling might not merely be a useful heuristic, but a principled method to address problems of adaptive sequential decision-making and causal inference.

ei

DOI [BibTex]

DOI [BibTex]


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Assessing randomness and complexity in human motion trajectories through analysis of symbolic sequences

Peng, Z, Genewein, T, Braun, DA

Frontiers in Human Neuroscience, 8(168):1-13, March 2014 (article)

Abstract
Complexity is a hallmark of intelligent behavior consisting both of regular patterns and random variation. To quantitatively assess the complexity and randomness of human motion, we designed a motor task in which we translated subjects' motion trajectories into strings of symbol sequences. In the first part of the experiment participants were asked to perform self-paced movements to create repetitive patterns, copy pre-specified letter sequences, and generate random movements. To investigate whether the degree of randomness can be manipulated, in the second part of the experiment participants were asked to perform unpredictable movements in the context of a pursuit game, where they received feedback from an online Bayesian predictor guessing their next move. We analyzed symbol sequences representing subjects' motion trajectories with five common complexity measures: predictability, compressibility, approximate entropy, Lempel-Ziv complexity, as well as effective measure complexity. We found that subjects’ self-created patterns were the most complex, followed by drawing movements of letters and self-paced random motion. We also found that participants could change the randomness of their behavior depending on context and feedback. Our results suggest that humans can adjust both complexity and regularity in different movement types and contexts and that this can be assessed with information-theoretic measures of the symbolic sequences generated from movement trajectories.

ei

DOI [BibTex]

DOI [BibTex]

2003


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Concentration Inequalities for Sub-Additive Functions Using the Entropy Method

Bousquet, O.

Stochastic Inequalities and Applications, 56, pages: 213-247, Progress in Probability, (Editors: Giné, E., C. Houdré and D. Nualart), November 2003 (article)

Abstract
We obtain exponential concentration inequalities for sub-additive functions of independent random variables under weak conditions on the increments of those functions, like the existence of exponential moments for these increments. As a consequence of these general inequalities, we obtain refinements of Talagrand's inequality for empirical processes and new bounds for randomized empirical processes. These results are obtained by further developing the entropy method introduced by Ledoux.

ei

PostScript [BibTex]

2003


PostScript [BibTex]


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Statistical Learning Theory, Capacity and Complexity

Schölkopf, B.

Complexity, 8(4):87-94, July 2003 (article)

Abstract
We give an exposition of the ideas of statistical learning theory, followed by a discussion of how a reinterpretation of the insights of learning theory could potentially also benefit our understanding of a certain notion of complexity.

ei

Web DOI [BibTex]


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Dealing with large Diagonals in Kernel Matrices

Weston, J., Schölkopf, B., Eskin, E., Leslie, C., Noble, W.

Annals of the Institute of Statistical Mathematics, 55(2):391-408, June 2003 (article)

Abstract
In kernel methods, all the information about the training data is contained in the Gram matrix. If this matrix has large diagonal values, which arises for many types of kernels, then kernel methods do not perform well: We propose and test several methods for dealing with this problem by reducing the dynamic range of the matrix while preserving the positive definiteness of the Hessian of the quadratic programming problem that one has to solve when training a Support Vector Machine, which is a common kernel approach for pattern recognition.

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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The em Algorithm for Kernel Matrix Completion with Auxiliary Data

Tsuda, K., Akaho, S., Asai, K.

Journal of Machine Learning Research, 4, pages: 67-81, May 2003 (article)

ei

PDF [BibTex]

PDF [BibTex]


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Constructing Descriptive and Discriminative Non-linear Features: Rayleigh Coefficients in Kernel Feature Spaces

Mika, S., Rätsch, G., Weston, J., Schölkopf, B., Smola, A., Müller, K.

IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(5):623-628, May 2003 (article)

Abstract
We incorporate prior knowledge to construct nonlinear algorithms for invariant feature extraction and discrimination. Employing a unified framework in terms of a nonlinearized variant of the Rayleigh coefficient, we propose nonlinear generalizations of Fisher‘s discriminant and oriented PCA using support vector kernel functions. Extensive simulations show the utility of our approach.

ei

DOI [BibTex]

DOI [BibTex]


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Tractable Inference for Probabilistic Data Models

Csato, L., Opper, M., Winther, O.

Complexity, 8(4):64-68, April 2003 (article)

Abstract
We present an approximation technique for probabilistic data models with a large number of hidden variables, based on ideas from statistical physics. We give examples for two nontrivial applications. © 2003 Wiley Periodicals, Inc.

ei

PDF GZIP Web [BibTex]

PDF GZIP Web [BibTex]


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Feature selection and transduction for prediction of molecular bioactivity for drug design

Weston, J., Perez-Cruz, F., Bousquet, O., Chapelle, O., Elisseeff, A., Schölkopf, B.

Bioinformatics, 19(6):764-771, April 2003 (article)

Abstract
Motivation: In drug discovery a key task is to identify characteristics that separate active (binding) compounds from inactive (non-binding) ones. An automated prediction system can help reduce resources necessary to carry out this task. Results: Two methods for prediction of molecular bioactivity for drug design are introduced and shown to perform well in a data set previously studied as part of the KDD (Knowledge Discovery and Data Mining) Cup 2001. The data is characterized by very few positive examples, a very large number of features (describing three-dimensional properties of the molecules) and rather different distributions between training and test data. Two techniques are introduced specifically to tackle these problems: a feature selection method for unbalanced data and a classifier which adapts to the distribution of the the unlabeled test data (a so-called transductive method). We show both techniques improve identification performance and in conjunction provide an improvement over using only one of the techniques. Our results suggest the importance of taking into account the characteristics in this data which may also be relevant in other problems of a similar type.

ei

Web [BibTex]


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Use of the Zero-Norm with Linear Models and Kernel Methods

Weston, J., Elisseeff, A., Schölkopf, B., Tipping, M.

Journal of Machine Learning Research, 3, pages: 1439-1461, March 2003 (article)

Abstract
We explore the use of the so-called zero-norm of the parameters of linear models in learning. Minimization of such a quantity has many uses in a machine learning context: for variable or feature selection, minimizing training error and ensuring sparsity in solutions. We derive a simple but practical method for achieving these goals and discuss its relationship to existing techniques of minimizing the zero-norm. The method boils down to implementing a simple modification of vanilla SVM, namely via an iterative multiplicative rescaling of the training data. Applications we investigate which aid our discussion include variable and feature selection on biological microarray data, and multicategory classification.

ei

PDF PostScript PDF [BibTex]

PDF PostScript PDF [BibTex]


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An Introduction to Variable and Feature Selection.

Guyon, I., Elisseeff, A.

Journal of Machine Learning, 3, pages: 1157-1182, 2003 (article)

ei

[BibTex]

[BibTex]


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New Approaches to Statistical Learning Theory

Bousquet, O.

Annals of the Institute of Statistical Mathematics, 55(2):371-389, 2003 (article)

Abstract
We present new tools from probability theory that can be applied to the analysis of learning algorithms. These tools allow to derive new bounds on the generalization performance of learning algorithms and to propose alternative measures of the complexity of the learning task, which in turn can be used to derive new learning algorithms.

ei

PostScript [BibTex]

PostScript [BibTex]